Initial commit

Divers/renforcement4/joue.py

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+import gym
+import cv2
+import tensorflow as tf
+from tensorflow.keras import models, layers
+import numpy as np
+import time
+import matplotlib.pyplot as plot
+import os
+
+env=gym.make("MsPacman-v0")
+
+#model=tf.keras.models.load_model('my_model_v1')
+model=tf.keras.models.load_model('my_model_target')
+
+decalage_debut=90
+taille_sequence=6
+
+def transform_img(image):
+  result=np.expand_dims(image[:170, :, 0], axis=-1)
+  return result
+
+def joue():
+
+  ######
+  observations=env.reset()
+  vie=3
+  for i in range(decalage_debut-taille_sequence):
+    env.step(0)
+  tab_sequence=[]
+  for i in range(taille_sequence):
+    observation, reward, done, info=env.step(0)
+    img=transform_img(observation)
+    tab_sequence.append(img)
+  tab_sequence=np.array(tab_sequence, dtype=np.float32)
+  ######
+
+  tab_img=[]
+  score=0
+  vie=3
+  while True:
+    valeurs_q=model(np.expand_dims(np.concatenate(tab_sequence, axis=-1), axis=0))
+    action=int(tf.argmax(valeurs_q[0], axis=-1))
+    print(action+1, end=' ')
+    score+=min(reward, 10.)
+    if info['ale.lives']<vie:
+      print("XXX", end=" ")
+      vie-=1
+    if done:
+      print("\nSCORE:", score)
+      return tab_img, score
+    
+    observation, reward, done, info=env.step(action+1)
+    if reward>10:
+      print("MIAM", reward, end=" ")
+
+    img=transform_img(observation)
+    tab_sequence[:-1]=tab_sequence[1:]
+    tab_sequence[taille_sequence-1]=img
+    tab_img.append(observation)
+    
+score=0
+while score<1400:
+  start_time=time.time()
+  tab_img, score=joue()
+  print(time.time()-start_time)
+      
+for i in range(len(tab_img)):
+  cv2.imshow("Pacman", tab_img[i])
+  key=cv2.waitKey(20)
+  if key==ord('q'):
+    break
+